import pandas as pd
import matplotlib.pyplot as plt
from ydata_profiling import ProfileReport

from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler

bank_marketing_csv = "data/bank_marketing/bank-full.csv"

bank_data = pd.read_csv(bank_marketing_csv, sep=";")

bank_data

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	previous	poutcome	y
0	58	management	married	tertiary	no	2143	yes	no	unknown	5	may	261	1	-1	0	unknown	no
1	44	technician	single	secondary	no	29	yes	no	unknown	5	may	151	1	-1	0	unknown	no
2	33	entrepreneur	married	secondary	no	2	yes	yes	unknown	5	may	76	1	-1	0	unknown	no
3	47	blue-collar	married	unknown	no	1506	yes	no	unknown	5	may	92	1	-1	0	unknown	no
4	33	unknown	single	unknown	no	1	no	no	unknown	5	may	198	1	-1	0	unknown	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
45206	51	technician	married	tertiary	no	825	no	no	cellular	17	nov	977	3	-1	0	unknown	yes
45207	71	retired	divorced	primary	no	1729	no	no	cellular	17	nov	456	2	-1	0	unknown	yes
45208	72	retired	married	secondary	no	5715	no	no	cellular	17	nov	1127	5	184	3	success	yes
45209	57	blue-collar	married	secondary	no	668	no	no	telephone	17	nov	508	4	-1	0	unknown	no
45210	37	entrepreneur	married	secondary	no	2971	no	no	cellular	17	nov	361	2	188	11	other	no

45211 rows × 17 columns

init_report = ProfileReport(bank_data)

init_report.to_file(f'bank_marketing_EDA.html')

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init_report

numerical_bank_data = bank_data[["balance", "duration", "campaign", "pdays", "previous"]]

numerical_bank_data

	balance	duration	campaign	pdays	previous
0	2143	261	1	-1	0
1	29	151	1	-1	0
2	2	76	1	-1	0
3	1506	92	1	-1	0
4	1	198	1	-1	0
...	...	...	...	...	...
45206	825	977	3	-1	0
45207	1729	456	2	-1	0
45208	5715	1127	5	184	3
45209	668	508	4	-1	0
45210	2971	361	2	188	11

45211 rows × 5 columns

numerical_bank_data.describe()

	balance	duration	campaign	pdays	previous
count	45211.000000	45211.000000	45211.000000	45211.000000	45211.000000
mean	1362.272058	258.163080	2.763841	40.197828	0.580323
std	3044.765829	257.527812	3.098021	100.128746	2.303441
min	-8019.000000	0.000000	1.000000	-1.000000	0.000000
25%	72.000000	103.000000	1.000000	-1.000000	0.000000
50%	448.000000	180.000000	2.000000	-1.000000	0.000000
75%	1428.000000	319.000000	3.000000	-1.000000	0.000000
max	102127.000000	4918.000000	63.000000	871.000000	275.000000

std_scaler = StandardScaler().fit(numerical_bank_data)
minmax_scaler = MinMaxScaler().fit(numerical_bank_data)
robust_scaler = RobustScaler().fit(numerical_bank_data)

std_data = std_scaler.transform(numerical_bank_data)
minmax_data = minmax_scaler.transform(numerical_bank_data)
robust_data = robust_scaler.transform(numerical_bank_data)

std_df = pd.DataFrame(std_data, columns=["balance", "duration", "campaign", "pdays", "previous"])
minmax_df = pd.DataFrame(minmax_data, columns=["balance", "duration", "campaign", "pdays", "previous"])
robust_df = pd.DataFrame(robust_data, columns=["balance", "duration", "campaign", "pdays", "previous"])

std_df.describe()

	balance	duration	campaign	pdays	previous
count	4.521100e+04	4.521100e+04	4.521100e+04	4.521100e+04	4.521100e+04
mean	1.760208e-17	6.035001e-17	3.017500e-17	2.011667e-17	4.023334e-17
std	1.000011e+00	1.000011e+00	1.000011e+00	1.000011e+00	1.000011e+00
min	-3.081149e+00	-1.002478e+00	-5.693506e-01	-4.114531e-01	-2.519404e-01
25%	-4.237719e-01	-6.025167e-01	-5.693506e-01	-4.114531e-01	-2.519404e-01
50%	-3.002800e-01	-3.035165e-01	-2.465603e-01	-4.114531e-01	-2.519404e-01
75%	2.158743e-02	2.362370e-01	7.622994e-02	-4.114531e-01	-2.519404e-01
max	3.309478e+01	1.809470e+01	1.944365e+01	8.297431e+00	1.191360e+02

fig, axes = plt.subplots(3,2, figsize=(10,15))

axes[0,0].hist(numerical_bank_data["balance"], bins=10)
axes[0,0].set_title("Balance")

axes[0,1].hist(numerical_bank_data["duration"])
axes[0,1].set_title("Duration")

axes[1,0].hist(numerical_bank_data["campaign"])
axes[1,0].set_title("Campaign")

axes[1,1].hist(numerical_bank_data["pdays"])
axes[1,1].set_title("Pdays")

axes[2,0].hist(numerical_bank_data["previous"])
axes[2,0].set_title("Previous")

plt.show()

fig, axes = plt.subplots(3,2, figsize=(10,15))

axes[0,0].hist(std_df["balance"], bins=10)
axes[0,0].set_title("Balance")

axes[0,1].hist(std_df["duration"])
axes[0,1].set_title("Duration")

axes[1,0].hist(std_df["campaign"])
axes[1,0].set_title("Campaign")

axes[1,1].hist(std_df["pdays"])
axes[1,1].set_title("Pdays")

axes[2,0].hist(std_df["previous"])
axes[2,0].set_title("Previous")

plt.show()

fig, axes = plt.subplots(3,2, figsize=(10,15))

axes[0,0].hist(minmax_df["balance"], bins=10)
axes[0,0].set_title("Balance")

axes[0,1].hist(minmax_df["duration"])
axes[0,1].set_title("Duration")

axes[1,0].hist(minmax_df["campaign"])
axes[1,0].set_title("Campaign")

axes[1,1].hist(minmax_df["pdays"])
axes[1,1].set_title("Pdays")

axes[2,0].hist(minmax_df["previous"])
axes[2,0].set_title("Previous")

plt.show()

fig, axes = plt.subplots(3,2, figsize=(10,15))

axes[0,0].hist(robust_df["balance"], bins=10)
axes[0,0].set_title("Balance")

axes[0,1].hist(robust_df["duration"])
axes[0,1].set_title("Duration")

axes[1,0].hist(robust_df["campaign"])
axes[1,0].set_title("Campaign")

axes[1,1].hist(robust_df["pdays"])
axes[1,1].set_title("Pdays")

axes[2,0].hist(robust_df["previous"])
axes[2,0].set_title("Previous")

plt.show()